Is evaluative conditioning really uncontrollable? A comparative test of three emotion-focused strategies to prevent the acquisition of conditioned preferences.
Bibliographic record
Abstract
Evaluative conditioning (EC) is defined as the change in the evaluation of a conditioned stimulus (CS) because of its pairing with a valenced unconditioned stimulus (US). Counter to views that EC is the product of automatic learning processes, recent research has revealed various characteristics of nonautomatic processing in EC. The current research investigated the controllability of EC by testing the effectiveness of 3 emotion-focused strategies in preventing the acquisition of conditioned preferences: (a) suppression of emotional reactions to the US, (b) reappraisal of the valence of the US, and (c) facial blocking of emotional responses. Although all 3 strategies reduced EC effects on self-reported evaluations by impairing recollective memory for CS-US pairings, they were ineffective in reducing EC effects on an evaluative priming measure. Regardless of the measure, effective control did not depend on the level of arousal elicited by the US. The results suggest that the 3 strategies can influence deliberate CS evaluations through memory-related processes, but they are ineffective in reducing EC effects on spontaneous evaluative responses. Implications for mental process theories of EC are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".